This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy.

The following paper presents the evaluation of an artificially intelligent assistant system (DIAS) with a service-oriented chatbot as a central communication element. The conversational AI (Artificial Intelligence) is supposed to increase information transparency in higher education environments and thus support students, teachers, and administrative staff. The exploratory study had two objectives: first, we intended to find out about the usability and utility of the DIAS chatbot using the CUQ (Chatbot Usability Questionnaire) score and benchmark the results against other conversational agents. Secondly, we were interested in possible effects among the different variables of interest, which could contribute to further theory development of chatbots in education. The results show that the DIAS chatbot scored above average, and can support students in finding relevant information, particularly if they use the assistant frequently. Positive aspects included the intuitive use, a welcoming persona (expressed in design & language) and easy navigation. The negative feedback showed potential for improvement particularly in content quality and handling dialogue mistakes, which is a general shortcoming of conversational AI at this development stage. The results can be used as a guidance for future research and theory building. However, they must be considered carefully due to several study limitations.

In a current research project at the Ansbach University of Applied Science, an AI-based quiz function was created to serve as a voluntary student-oriented support offer to determine their learning progress in their respective courses by means of conducting self-assessment quizzes. The application takes lecture scripts as input and applies a question generation model to create questions that students can answer. In order to evaluate the given answers, another language model is involved to perform Natural Language Inference (NLI). Users can engage with the system via a graphical user interface currently provided via a web app. To assess preliminary feasibility and perception of the model prototype, a qualitative focus group discussion following a semi-structured interview guideline prepared by the research team according to similar studies in the education field (Sek et al. 2012) was conducted with five participants. A transcript of the discussion was prepared and analyzed using the qualitative content analysis method according to Kuckartz. Overall, the quiz function was well received by the participants of the focus group. However, the prototype still has potential when it comes to generating meaningful questions and transparently assigning categories to the given answers. Furthermore, the quiz parameters should be individually adjustable by users. In the following paper, the development of the service is illustrated by outlining the considerations for the application design and the training procedure of the language models. Afterwards, the design of the qualitative focus group is described including the presentation of the results.

Dieses essential gibt einen Überblick über verschiedene Go-to-Market-Strategien für zweiseitige digitale Plattform-Geschäftsmodelle. Zunächst liegt der Fokus auf den Strategien zur sequenziellen, simultanen und viralen Kundenakquise. Im zweiten Teil zeigt der Beitrag, mit welchen Faktoren man positive Interaktionen fördert, um eine kritische Masse an Nutzern zu gewinnen.

Ambidextrie im Innovationsmanagement bezieht sich auf die Fähigkeit eines Unternehmens, sowohl das Kerngeschäft zu stärken (Exploit) als auch mit neuen Angeboten in bestehenden oder neuen Märkten zu agieren (Explore). Dabei besteht die Herausforderung darin, beide Innovationsmodi in einer komplementären Art und Weise einzusetzen, anstatt sich auf einen der beiden Modi zu beschränken. Eine ambidextre Ausrichtung und die Fähigkeit, beide Innovationsmodi, Exploit und Explore, gleichzeitig und in dem Maß einzusetzen, das die Umstände erfordern, ermöglicht es Unternehmen, schnell auf Veränderungen in der Branche zu reagieren und sich langfristig im Markt zu behaupten. Für eine erfolgreiche Umsetzung von Ambidextrie im Innovationsmanagement spielen die Unternehmenskultur, der Führungsstil resp. das Führungsverständnis ebenso eine Rolle, wie Grundsatzentscheidungen in Bezug auf die Digitalisierung. Es ist entscheidend, dass Unternehmen die Potenziale von Ambidextrie erkennen und lernen, den Exploit- und Explore-Modus gezielt und optimal einzusetzen, um langfristig Wettbewerbsvorteile zu erzielen. Dieser Grundlagenartikel fokussiert insbesondere auf die im weiteren Heft weniger angesprochenen Themenbereiche. Er ordnet die Ambidextrie im Innovationsmanagement anhand der Themenfelder Kultur, Führung und Digitalisierung ein und liefert Argumente, weshalb die Akzeptanz und Nutzung von Ambidextrie zu Wettbewerbsvorteilen führen.

Entrepreneurship in the form of business foundations, especially start-ups, drives progress and innovation on many levels. They are currently one of the critical drivers of economic prosperity in our society. More and more economic policy measures are aimed at promoting just this. However, strong regional differences are observed in start-up activity. Thus, the support of start-ups and the development of so-called entrepreneurial ecosystems are also playing an increasingly important role in economic and regional policy. It is necessary to look at each region singularly, to identify particularities, and to determine the needs of regional actors to derive suitable measures for establishing such an ecosystem. This article examines an exemplary region for specific determinants by applying a start-up ecosystem model. Strengths, weaknesses, and potentials of the business location about its framework conditions for start-ups are identified in two group interviews (founders and supporting institutions). The results show that the numerous interactions of a start-up ecosystem are not sufficiently targeted and efficiently managed in the current structures and that relevant actors perceive the effectiveness of ecosystem elements and support measures in a differentiated manner. The study was able to provide insights for future start-up and innovation policy measures and interventions in the region studied and are thus intended to serve methodologically as a role model for the analysis of other regional start-up ecosystems.

Due to migration, terror-threats and the viral pandemic, various EU member states have re-established internal border control or even closed their borders. European Association for Biometrics (EAB), a non-profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re-establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re-establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade-off with regards to open borders while maintaining a high-level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions.

Automated fingerprint recognition systems, while widely used, are still vulnerable to presentation attacks (PAs). The attacks can employ a wide range of presentation attack species (i.e., artifacts), varying from low-cost artifacts to sophisticated materials. A number of presentation attack detection (PAD) approaches have been specifically designed to detect and counteract presentation attacks on fingerprint systems. In this chapter, we study and analyze the well-employed Convolutional Neural Networks (CNN) with different architectures for fingerprint PAD by providing an extensive analysis of 23 different architectures in CNNs. In addition, this chapter presents a new approach introducing vision transformers for fingerprint PAD and validates it on two different public datasets, LivDet2015 and LivDet2019, used for fingerprint PAD. With the analysis of vision transformer-based F-PAD, this chapter covers both spectrum of CNNs and vision transformers to provide the reader with a one-place reference for understanding the performance of various architectures. Vision transformers provide at par results for the fingerprint PAD compared to CNNs with more extensive training duration suggesting its promising nature. In addition, the chapter presents the results for a partial open-set protocol and a true open-set protocol analysis where neither the capture sensor nor the material in the testing set is known at the training phase. With the true open-set protocol analysis, this chapter presents the weakness of both CNN architectures and vision transformers in scaling up to unknown test data, i.e., generalizability challenges.

Biometric systems have experienced a large development over the last years since they are accurate, secure and in many cases, more user convenient than traditional credential-based access control systems. In spite of their benefits, biometric systems are vulnerable to attack presentations, which can be easily carried out by a non-authorised subject without having a deep computational knowledge. This way, he/she can gain access to several applications where biometric systems are frequently deployed, such as bank accounts and smartphone unlocking. In order to mitigate such threats, we present in this work a study on the feasibility of using the Fisher Vector (FV) representation to spot unknown-attack presentations over different biometric modalities such as fingerprint, face and voice. By learning a common feature space from a set of local features, extracted from known samples, the FVs lead to the construction of reliable discriminative models which can successfully distinguish a bona fide presentation from an attack presentation. The experimental evaluation over publicly available databases (i.e. LivDets, CASIA-FASD, SiW-M and ASVspoof, among others) yields error rates outperforming most state-of-the-art algorithms for challenging scenarios where species, recipies or capture devices remain unknown.

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real-world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats, presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as the presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere with its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different levels of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarize the most popular types of attacks including the main challenges to address. Second, we present a taxonomy of PAD methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into iris recognition systems according to the most important scenarios of practical application.

Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations, there are growing concerns about the associated environmental costs. Energy-efficient Deep Learning has received much attention from researchers and has already made much progress in the last couple of years. This paper aims to gather information about these advances from the literature and show how and at which points along the lifecycle of Deep Learning (IT-Infrastructure, Data, Modeling, Training, Deployment, Evaluation) it is possible to reduce energy consumption.

In the past decade, globalization and digitization have not only changed the way we work, but also the environment in which we work. More and more companies are introducing desk sharing office concepts in which employees must share a workstation. However, this poses challenges for ergonomic workplace design as constant and ergonomically correct workstation settings can hardly be guaranteed. Neglecting ergonomics at workplace, though, can cause musculoskeletal disorders. Therefore, a concept and prototype for a system are proposed which automatically adjusts the workstation to the individual's anthropometric characteristics. A setup of different mechanical and electronical components using microcontrollers, ultrasonic distance sensors and linear actuators assures an automatic adjustment where users only must sign in with their ID. An initial field study shows that the system can achieve high user acceptance. Simplicity, speed, and convenience are seen as added value of the system. The results have potential for future studies.

Artificial Intelligence (AI) is drastically transforming the world around us. Rather than replacing humans, hybrid intelligence combines human and machine intelligence to leverage each of their individual strengths. We summarize different requirements and approaches identified to achieve hybrid intelligence and focus on conversational AI to build a cognitive agent that supports knowledge management within an organization. The agent automatically extracts knowledge from artifacts provided or published by the us- ers. In addition, the knowledge base steadily grows while the agent talks to the users and the users provide feedback and the system is continuously learning to extract new types of entities and relations to answer more questions based on the knowledge graph and to access other sources of information. The first types of entities and relations extracted already support users in finding colleagues with relevant skills or inter- ests. Based on information provided by the agent, collaboration among employees and, thus, knowledge sharing and transfer is encouraged. The collaboration between the cognitive agent as an AI artifact and employees combined with a system that learns and adapts while in use stressing explainability and trust in its answers entails a step towards hybrid intelligence.